Yuji Sang, Chenglong Xu, Long Lv, Lijun Liu · 5 authors
Abstract Aiming at the security issues of open channel vulnerability, limited node resources, and single-point failure caused by centralized authentication in unmanned aerial vehicle (UAV) swarm networks, this paper proposes an authentication and key agreement scheme integrating Physical Unclonable Function (PUF), Chebyshev chaotic map and blockchain. The scheme constructs an integrated architecture of physical security, lightweight encryption and distributed trust, which supports mutual authentication in dual scenarios of UAV-Ground Control Station (GCS) and UAV-UAV. Decentralized trusted authentication is realized via blockchain and smart contracts, ensuring that authentication information is tamper-proof and traceable. Formal security verification based on the ROR model and informal analysis demonstrate that the proposed scheme satisfies multiple security requirements including anonymity and forward secrecy, and can resist common attacks such as replay attack, man-in-the-middle attack and physical capture attack. Performance evaluation results indicate that the scheme completes authentication with only two rounds of interaction. Its computational and communication overheads are significantly lower than those of existing schemes, making it suitable for resource-constrained UAV swarms.
Decentralized unmanned aerial vehicle (UAV) swarms require low-latency peer communication while remaining resilient to spoofing, replay, command injection, key compromise, and malicious membership changes. This study develops a zero-trust communication framework that separates the real-time swarm data plane from a permissioned Byzantine-fault-tolerant trust ledger. The method is grounded in an existing GPS-denied UAV software baseline implementing canonical packet hashing, HMAC-SHA256 authentication, trust epochs, timestamp and sequence freshness checks, onboard security-state transitions, command-policy gating, and firmware trust records. The proposed extension introduces per-node identities, authenticated session establishment, AEAD-protected peer traffic, and event-sparse ledger anchoring for trust-changing evidence. Formal models are derived for message acceptance, trust dynamics, Byzantine tolerance, consensus traffic, storage growth, processing overhead, and energy cost. Under a representative analytical case of 100 swarm messages/s, a 1% anchoring ratio reduces ledger event rate and modeled consensus-control traffic by 100 times compared with per-packet anchoring. Repository benchmark measurements are reported separately from blockchain projections. The analysis supports using blockchain as a decentralized trust anchor rather than as a transport for flight-critical telemetry.
Aircraft maintenance records are critical to airworthiness and asset valuation, yet they are often fragmented across stakeholders, creating verification bottlenecks and information asymmetry that may suppress aircraft residual value. This paper proposes a blockchain-anchored decentralized application (dApp) based on a dual-layer architecture that combines InterPlanetary File System (IPFS)-based off-chain storage with on-chain anchoring of Content Identifiers (CIDs) and selected metadata. With respect to off-chain file size, the on-chain payload per record remains $\mathcal{O}(1)$, compared with $\mathcal{O}(n)$ for direct on-chain file storage. The architecture incorporates metadata and traceability controls informed by Federal Aviation Administration (FAA) electronic recordkeeping guidance. The main contribution is an economic framework that models the relationship between tamper-evident maintenance-record provenance, audit workflow duration, aircraft residual value, and operational cost. In a 7-kB experiment conducted on the BNB Smart Chain testnet, CID anchoring reduced gas consumption by 93.9\% compared with direct on-chain storage. Under explicitly stated scenario assumptions, the audit-cost model indicates potential savings of more than 90\%. These results support the technical feasibility of the prototype and illustrate its economic potential, while the estimated financial benefits remain to be validated using operational data.
A privacy-preserving compliance audit architecture for unmanned aerial vehicle (UAV) swarm operations. The central contribution is a deconfliction-to-containment reduction: rather than comparing n trajectories after the fact (a quadratic, disclosure-bound check), a planner assigns pairwise-disjoint spatial tubes before take-off and establishes their separation once, so that each vehicle subsequently attests only that its own samples stayed inside its own tube. Collision-freedom follows as a consequence (Theorem 2), and the pairwise cost is paid a single time at planning. The commitment layer (Layer 1) is implemented and evaluated as a decision-support audit pipeline that produces non-disclosing, tamper-evident audit artifacts via pre-flight Merkle commitments. It is evaluated in an emulated UAV swarm environment with systematic adversarial injection, in configurations up to 200 vehicles × 500 samples (100,000 sample statements), reporting artifact size, commit/prove/verify/disjunction times, and tamper-detection rates. We then formally identify the security boundary of the implemented layer: it provides coordinate hiding and tamper evidence, but cannot by itself make self-reported containment truthful, which we state as a security game and an impossibility result (Theorem 3). We specify the additional soundness layers (range proof, continuity, provenance and freshness, and aggregation) needed for full containment assurance, proving that composing a knowledge-sound range argument closes the gap (Theorem 4). Throughout, we separate the implemented and measured Layer 1 from the specified and proved—but not yet benchmarked—Layers 2–4, and we make no claim of full zero-knowledge geofence compliance, of swarm-scale deployment, or of deployment readiness.
The aviation industry depends on data integrity across supply chains spanning OEMs, MRO organizations, airlines, lessors, and national regulators. Centralized data management systems — still dominant in the sector — expose the ecosystem to single points of failure and provide limited traceability of millions of aircraft parts circulating annually. This paper presents a structured review of blockchain-based security architectures for aviation networks, synthesized from 14 peer-reviewed sources published between 2018 and 2025, retrieved from IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and Wiley/Hindawi. On this basis, a thirteen-step design method is proposed for integrating permissioned blockchain with distributed cloud infrastructure in aviation environments. The method is grounded in quantitative acceptance criteria — throughput ≥ 500 TPS, smart contract execution latency < 200 ms (p95), system availability 99.9% — and maps each design phase to specific security controls (integrity, access control, auditability, privacy, resilience, governance). Core mechanisms are formalized via hash-chain integrity verification, attribute-based access control functions, zero-knowledge proof verification, and a composite pre-ledger trust-scoring model. The principal finding: permissioned blockchain architectures — Hyperledger Fabric in particular — can support aviation requirements for immutable audit trails, decentralized identity management, and regulatory compliance with EASA and FAA; adoption remains constrained by organizational readiness and the unresolved GIGO problem at the ledger boundary.
The high-level integration of generative artificial intelligence (AI) in edge computing systems has raised the question of the integrity and reliability of deploying Model-as-a-Service. Edge servers are not required to follow the so-called generative model to minimize computational cost, whereas users and service providers want validation mechanisms that do not compromise proprietary model information. To address this challenge, this study proposes a cooperative unmanned aerial vehicle (UAV)-swarm-enabled zero-knowledge verification framework for secure, privacy-preserving verification of edge-based generative artificial intelligence inference. The proposed framework involves edge servers producing an interactive cryptographic zero-knowledge proof to verify the execution of generative AI, and UAV swarms that fly freely to confirm verification operations, subject to mobility and energy constraints. The age of verification metric is proposed to trust verification information, jointly reflecting the unverified server reliability and verification freshness, and to provide dynamic priority to risky edge servers. To effectively plan the behaviour of a UAV swarm, a trust-based multi-agent reinforcement learning approach is developed that enables decentralized decision-making while training is centralized. Extensive simulation results show that the proposed framework significantly improves the state-of-the-art baseline schemes in verification timeliness, malicious server detection delay, energy efficiency, and scalability. The findings validate that integrating cooperative UAV swarms, trust-aware verification, and multi-agent learning is an efficient approach to providing reliable generative AI services in dynamic edge computing environments.
О. М. Литвинов, О. В. Чуприна, В. О. Гребеніков, М. В. Чуприна
The article explores the concept of containerized mobile hubs as an innovative solution for the infrastructural support of autonomous operations integrating civil aviation and civil unmanned aerial vehicles (UAVs). The relevance of transitioning to flexible, decentralized, and highly automated solutions is substantiated in the context of the development of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM), which require new approaches to ground infrastructure organization. The key problem is identified as the infrastructure gap between the rapid advancement of UAV technologies and the limited capabilities of traditional aeronautical systems.A concept of a mobile hub based on a standardized ISO container (in particular, High Cube or refrigerated type) is proposed. The hub performs the functions of power supply, dispatching, communication, maintenance, and charging of exclusively civil and commercial UAVs. The hub is considered as an intelligent node integrated with civil UTM/U-space systems, ensuring the coordination of manned and unmanned flights within a unified digital airspace. The scope of practical application of the complex is outlined, encompassing environmental monitoring, support for humanitarian missions, and civil protection operations. Existing commercial "drone-in-a-box" solutions are analyzed, and their limitations are identified, including narrow functionality and insufficient mobility.Special attention is paid to the technical aspects of hub implementation, including container design selection, climate control, energy efficiency, and rapid deployment capabilities in environments where stationary infrastructure is absent or damaged. It is demonstrated that the proposed approach significantly reduces operational costs and enables continuous 24/7 operations of civil UAVs.It is concluded that containerized mobile hubs can become a key element in forming a new decentralized aeronautical infrastructure for civil aviation, particularly in the context of the peaceful recovery of Ukraine's transport sector, providing rapid deployment, versatility, and a high level of autonomy.
Open access
UAV Applications and Optimization
Military Technology and Strategies
Advanced Control and Stabilization in Aerospace Systems
As unmanned aerial vehicles (UAVs) become increasingly integral in domains such as agriculture, logistics, and military operations, secure cross-domain authentication mechanisms are essential. Existing centralized protocols are prone to single points of failure, privacy vulnerabilities, and physical capture risks. This paper presents a novel blockchain-based, privacy-preserving authentication protocol for UAVs operating across multiple domains. By combining zero-Knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) and physical unclonable functions (PUFs), the proposed protocol ensures secure identity verification without disclosing sensitive information. The blockchain platform offers a decentralized, tamper-resistant environment for UAV authentication, addressing the challenges of scalability, privacy, and security in cross-domain operations. We demonstrate the security and effectiveness of the protocol through formal and informal security proofs and performance evaluations. The results indicate that the proposed protocol outperforms traditional methods, achieving significant reductions in both computational and communication costs while maintaining high security standards.
Sufian Al majmaie, Ghazal Ghajari, Niraj Prasad Bhatta, Fathi Amsaad
The integration of Fog Computing with Flying Ad-Hoc Networks (FANETs) offers promising capabilities for decentralized, low-latency intelligence in UAV-based applications. However, the distributed nature, mobility, and resource constraints of FANETs expose them to significant security and privacy challenges, particularly against quantum threats. To address these issues, this work introduces a blockchain-based, AI-enhanced key management framework designed for fog-enabled FANETs. The proposed scheme employs a Post-Quantum Multivariate Identity-Based Signature Scheme (PQ-MISS) and Zero-Knowledge Proofs (ZKPs) to achieve secure key establishment, privacy-preserving data aggregation, and integrity verification. A polynomial composition-based encryption mechanism and an aggregate signature model support secure and efficient multi-device communication across fog and UAV layers. Fog servers construct partial blockchain blocks from validated UAV data. These blocks are completed and mined by Cloud Servers (CSs). AI algorithms then analyze the verified data to generate accurate predictions and insights. NS-3 simulations validate the efficiency of PQ-MISS in reducing communication overhead while improving the speed and reliability of data aggregation and verification. Comparative analysis demonstrates the proposed scheme's advantages over existing methods in computational cost, post-quantum security, and scalability, making it a robust solution for secure, intelligent, and future-ready FANET systems.
The following paper presents research aimed at identifying the most critical risks and their mitigations in Urban Air Mobility (UAM) operations. This topic is one of aviation's most significant challenges in the coming decades. Having many flying vehicles in a single airspace requires an innovative approach, rule redefinition, and traffic management. Some solutions are scalable and can be adapted from general aviation. Therefore, stakeholders must address new risks and implement dedicated methods while maintaining the highest level of operational safety. Simulation research is needed to validate solutions before systems operate in real environments. The response to those challenges is the development of a simulation tool that can serve as a test benchmark. The study is divided into two sections: identifying potential risks associated with the rapidly growing UAV market and its applications in urban environments and developing a simulation tool that addresses various Urban Air Mobility challenges. A set of test cases is presented to demonstrate the tool’s functionality and capabilities for further analysis. The paper reviews the United States and European Union approaches to UAM integration, including NASA, FAA, SESAR, and EASA initiatives, and highlights differences in operational concepts and regulatory frameworks. The research identifies major categories of risks related to UAV operations, including technical failures, environmental hazards, human factors, and cybersecurity threats. Long-term challenges associated with increasing traffic density, autonomous operations, and airspace organization are also discussed. The research evaluates scalable safety solutions derived from commercial aviation and analyzes urban airspace concepts such as layers, zones, sky-lanes, and sky-corridors. The developed simulation environment, implemented for the Warsaw metropolitan area, enables modeling of large-scale UAV and VTOL operations, no-fly zones, vertiport hubs, and traffic distribution. The results demonstrate the importance of dedicated traffic structures, altitude separation, and decentralized traffic management systems in ensuring safe and efficient Urban Air Mobility operations.
Secure cross-domain UAV authentication is challenging because identity verification alone is insufficient to guarantee safe operation. In many UAV applications, it is equally critical to verify that a UAV is currently located within an authorized geographic region. Existing approaches often expose precise GPS coordinates, rely on static identifiers that enable tracking, or fail to guarantee the freshness and authenticity of location evidence. These weaknesses allow replay, location spoofing, and trajectory inference attacks, especially in multi-domain environments. To address these limitations, we propose PrivLocAuth, a zero-knowledge-based cross-domain UAV authentication protocol that enforces geofence restrictions without revealing actual locations. In PrivLocAuth, UAVs encode their current coordinates into fresh Pedersen commitments, which are attested by the home Local Domain Server (LDS) using short-lived Schnorr signatures. Based on these attested commitments, UAVs generate Bulletproof range proofs to demonstrate compliance with cross-domain server-defined geofences. This design ensures that UAVs operate within authorized airspace while preserving strong location privacy. PrivLocAuth further incorporates a lightweight elliptic curve cryptography (ECC) and Schnorr signature-based credential framework that enables unlinkable authentication across-domains, preventing session correlation and identity tracking. Formal security analysis demonstrates resistance to impersonation, replay, geofence-bypass, and linkage attacks. Experimental evaluation shows low computational latency and minimal communication overhead, confirming the protocol’s suitability for resource-constrained UAV platforms operating in dynamic cross-domain environments.
SATHISHKUMAR RANGANATHAN, Muralindran Mariappan, M. Karthigayan
Swarm robotics is an emerging field capable of accomplishing complex tasks through collective behaviour. However, it continues to face persistent challenges in secure communication, decentralized decision-making, and scalability. To operate effectively in resource-constrained environments, swarm networks require a decentralized mechanism that is secure, fast, and efficient. Although many studies have explored the use of blockchain technology for swarm robotics, existing blockchain consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and their variants remain unsuitable due to high computational complexity and risk of stake centralization. To address these challenges, we introduce the blockchain-based Rotational Leadership Role (RLR) consensus algorithm, a voting-based consensus re-engineered from the Raft approach, together with Decentralized Task Authorization and Validation (DeTAV), a token-based mechanism for context-aware task validation. This design ensures efficiency, security, and scalability in swarm robotics and drone systems. RLR is lightweight and well suited to operate within the limited computing resources of small robots or aerial drones. To validate its performance, a custom-built robotic simulator was developed as part of this research. Experiments conducted with up to 70 concurrent robots demonstrated that RLR consumed under 90 MB Random Access Memory (RAM) and 12% Central Processing Unit (CPU), whereas PoW required 460 MB RAM and 27% CPU with a minimum difficulty level of 21, reflecting an 80% reduction in memory usage and a 55% reduction in CPU consumption. Scalability tests with 4 to 70 robots further revealed RLR’s scalability with an average of 78% higher throughput, 47% lower election latency, and 34% lower consensus latency. Additionally, under the simulated attack scenarios and assuming uncompromised cryptographic keys, DeTAV’s context-based validation consistently achieved 100% success in detecting and isolating Byzantine nodes, while reducing Quality of Detection (QoD) time by 67%. Collectively, these results confirm that RLR with DeTAV effectively meets the efficiency, security, and scalability requirements of swarm robotic and drone networks.
Digital twins are digital representations that enable real-time monitoring, analysis, andprediction of outcomes of physical systems. They depend on continuous communicationto work, which increases the attack surface of the system and introduces security risks,especially regarding unauthorized access to digital twin data and operations. This thesisinvestigates how blockchain-based smart contracts can be used as an authorization mech-anism for a digital twin, by implementing a digital twin for a Crazyflie 2.1 and controllingaccess to it through a smart contract-based authorization layer.A prototype of this system was developed using Python and connected to the physicalUAV using the Crazyradio interface. Flight data was collected and used to identify a sim-plified digital twin representing the vertical subsystem. A blockchain-based authorizationlayer with role-based permissions was then implemented using Solidity smart contracts ina local Hardhat environment.The findings from this thesis show that such a system is feasible to implement. Flighttest runs show that the twin remained numerically stable at all times and estimated thephysical UAV’s state with bounded error. The authorization mechanism enforced the de-fined role-based access-control rules in the tested scenarios, with measured authorizationlatency in the local environment around 14–15 ms. Gas measurements were also used toestimate the relative computational cost of the smart contract operations.
Abdullah Aljumah, Tariq Ahamed Ahanger, Imdad Ullah
Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains such as surveillance, logistics, and disaster management. However, ensuring the safety, security, and trustworthiness of UAV operations remains a significant challenge, primarily due to vulnerabilities in centralized data processing architectures. Traditional UAV systems rely on remote cloud servers to perform machine learning (ML)-based analytics, which introduces issues such as data exposure, latency, scalability bottlenecks, and susceptibility to cyberattacks during data transmission and storage. These challenges underscore the urgent need for a decentralized, verifiable, and privacy-preser ving learning mechanism that can support collaborative UAV intelligence without centralized control. To address these limitations, this study proposes a blockchain-enabled distributed ML framework that facilitates secure, peer-to-peer collaboration among UAV nodes. The framework integrates blockchain’s immutable ledger and smart contracts with decentralized ML models, enabling UAVs to share and validate trained models rather than raw data. This ensures data confidentiality, integrity, and transparency throughout the learning process. A stacking-based ensemble mechanism is employed to enhance predictive performance through collaborative knowledge aggregation. The proposed system is experimentally validated using a collaborative intrusion detection (ID) scenario using the KDD99 network attack data set and real-world implementation. The results demonstrate significant improvements in detection accuracy, latency and F1-score compared to conventional centralized ML methods, achieving an average accuracy of 97.9%, latency 198ms, and F1-score exceeding 97%. These outcomes confirm that the integration of blockchain and decentralized ML effectively mitigates cybersecurity risks while enabling scalable, trustworthy UAV intelligence.
Drone delivery services are encountering issues related to transparency, authenticity, and safeguarding privacy, highlighting the urgent need for an innovative approach that incorporates blockchain technology. This innovation aims to solidify the permanence of records, enable instantaneous verification, and streamline data handling in these intricate, self-operating transactions. In this paper, we use of blockchain for creating Non-Fungible Tokens (NFTs), which act as unalterable logs of purchase within the realm of delivery logistics. Our method adopts a distinctive two-fold strategy that places equal emphasis on both tangible goods and information. When integrating our solution with the Polygon network, we have achieved a substantial reduction in the costs associated with transactions while simultaneously enhancing the speed at which these transactions are processed. Our work not only addresses the existing challenges faced by unmanned aerial vehicle (UAV) communication systems but also sets a new standard for efficiency and security in the delivery logistics sector, paving the way for more reliable and transparent UAV-based delivery services.
The integration of Uncrewed Aerial Vehicles (UAVs) into low-altitude airspace has led authorities to adopt distributed Uncrewed Traffic Management (UTM) architectures that ensure interoperability and safety. Blockchain has been proposed as an enabler for trustworthy coordination among UTM stakeholders. Yet, its real-time performance under aeronautical constraints remains insufficiently characterized. This paper presentes a quantitative benchmark comparing two regulation compliant distributed architectures: the federated InterUSS platform maintained by the Linux Foundation and a permissioned blockchain based on Hyperledger Fabric. Both systems were evaluated through Operational Intent Reference (OIR) registration work loads generated via Hyperledger Caliper, measuring throughput, latency, and transaction loss under loads up to 50 transactions per second. Results show that InterUSS sustained sub-second latency and stable performance up to 30 TPS. At the same time, Fabric exhibited exponential degradation with median latency exceeding 3 s and tail latencies above 15 s beyond that point. These findings demonstrate that blockchain-based architectures must be redesigned to meet aeronautical timing and scalability requirements, suggesting that hybrid models combining distributed ledgers for auditability with federated frameworks for real-time coordination are more suitable for future UTM deployments.
The proliferation of unmanned aerial vehicle (UAV) swarms in mission-critical applications for 6G and the Internet of Things (IoT) introduces significant security vulnerabilities stemming from their dynamic, distributed, and resource-constrained nature. Traditional security paradigms are often inadequate for these complex cyber-physical systems. This paper proposes a novel, cross-layer security framework that ensures robust and lightweight operation for UAV swarms. The framework is founded on a novel Entropy-Derived Physically Unclonable Function (EPUF) based on DRAM, which employs a data-driven characterization process designed to achieve near 100% reliability in simulation through a data-driven characterization process, which is validated through extensive simulation, addressing a critical limitation of conventional PUFs. To counteract sophisticated threats, we formulate the key management problem as a Markov Decision Process (MDP) and introduce a deep reinforcement learning (DRL) agent that dynamically optimizes key update frequency, balancing security posture against energy consumption. Furthermore, we leverage a lightweight, permissioned blockchain as a decentralized trust anchor for public key management, providing an immutable and resilient ledger and enhancing the principles of distributed and edge intelligence. The core authentication protocol's security is formally verified using the ProVerif tool and Belief Logic, proving its robustness against a Dolev-Yao adversary. Experimental simulations demonstrate that our framework significantly outperforms conventional methods, reducing authentication latency and energy consumption by over 95% compared to PKI-based schemes while effectively mitigating replay and impersonation attacks.
Md Bokhtiar Al Zami, Md Raihan Uddin, Dinh C. Nguyen
Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UAVs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UAV network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. To optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UAV flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UAV-based intelligent networks.
Haoxiang Luo, Ruichen Zhang, Yinqiu Liu, Gang Sun · 6 authors
Low-altitude airspace is becoming a new frontier for smart city services and commerce. Networks of drones, electric Vertical Takeoff and Landing (eVTOL) vehicles, and other aircraft, termed Low-Altitude Economic Networks (LAENets), promise to transform urban logistics, aerial sensing, and communication. A key challenge is how to efficiently share and trust the computing utility, termed “computility”, of these aerial devices. We propose treating the computing power on aircraft as tokenized Real-World Assets (RWAs) that can be traded and orchestrated via blockchain. By representing distributed edge computing resources as blockchain tokens, disparate devices can form Low-Altitude Computility Networks (LACNets), collaborative computing clusters in the sky. We first compare blockchain technologies, non-fungible tokens (NFTs), and RWA frameworks to clarify how physical hardware and its computational output can be tokenized as assets. Then, we present an architecture using blockchain to integrate aircraft fleets into a secure, interoperable computing network. Furthermore, a case study models an urban logistics LACNet of delivery drones and air-taxis. Simulation results indicate improvements in task latency, trust assurance, and resource efficiency when leveraging RWA-based coordination. Finally, we discuss future research directions, including AI-driven orchestration, edge AI offloading and collaborative computing, and cross-jurisdictional policy for tokenized assets.
Cooperative multi-UAV clusters have been widely applied in complex mission scenarios due to their flexible task allocation and efficient real-time coordination capabilities. The Air Command Aircraft (ACA), as the core node within the UAV cluster, is responsible for coordinating and managing various tasks within the cluster. When the ACA undergoes fault recovery, a handover operation is required, during which the ACA must re-authenticate its identity with the UAV cluster and re-establish secure communication. However, traditional, centralized identity authentication and ACA handover mechanisms face security risks such as single points of failure and man-in-the-middle attacks. In highly dynamic network environments, single-chain blockchain architectures also suffer from throughput bottlenecks, leading to reduced handover efficiency and increased authentication latency. To address these challenges, this paper proposes a mathematically structured dual-chain framework that utilizes a distributed ledger to decouple the management of identity and authentication information. We formalize the ACA handover process using cryptographic primitives and accumulator functions and validate its security through BAN logic. Furthermore, we conduct quantitative analyses of key performance metrics, including time complexity and communication overhead. The experimental results demonstrate that the proposed approach ensures secure handover while significantly reducing computational burden. The framework also exhibits strong scalability, making it well-suited for large-scale UAV cluster networks.
Ahmed Alagha, Maha Kadadha, Rabeb Mizouni, Shakti Singh · 6 authors
This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). Recently, UAVs have gained popularity as relay nodes to complement vehicles in IoV networks due to their ability to extend coverage through unbounded movement and superior communication capabilities. The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination processes are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for efficient and autonomous UAV coordination, and finally, a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the coordination between the selected UAVs is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). MDRL offers decentralized control and intelligent decision-making for the UAVs to maintain coverage and connectivity over the assigned vehicles. The evaluation results demonstrate that the proposed selection mechanism improves the stability of the selected relays, while MDRL maximizes the coverage and connectivity achieved by the UAVs. Both methods show superior performance compared to several benchmarks.
Unmanned aerial vehicles (UAVs) have witnessed significant growth in various domains, such as agriculture, disaster management, and remote health management systems. However, the use of UAVs necessitates secure and efficient solutions that uphold privacy during task distribution. To address this challenge, this article introduces a novel architecture for privacy-aware task distribution in UAV communication systems. Our approach leverages the benefits of blockchain and smart token-based identification within the proposed architecture, ensuring decentralized, transparent, and tamper-proof operations. By adopting a crowdsourced task distribution model, our approach further optimizes task assignment among UAVs while prioritizing data privacy, user access control, and scalability. The architecture is designed to enhance fault tolerance, enabling seamless operation under dynamic and unpredictable conditions. We present a comprehensive implementation details of a proof-of-concept prototype of our proposed architecture, detailing its design and functionality. The experimental results demonstrate the feasibility, efficiency, and adaptability of our approach in diverse real-world scenarios, highlighting its potential for broader adoption across UAV applications.
Joel Curado, Manila Bhandari, João C. Ferreira, Ana Martins
The maritime supply chain plays a vital role in global trade, but it continues to face major challenges, including transparency issues, fraud, and data privacy concerns. Blockchain technology has emerged as a promising solution to make the supply chain more secure, efficient, and trustworthy across the system. However, it still encounters limitations, especially regarding privacy and the handling of large volumes of data. To address these issues, Zero-Knowledge Proofs (ZKPs) offer a viable solution, enabling the validation of documents and transactions without revealing sensitive information. This helps maintain confidentiality while meeting regulatory requirements, such as those set by the eFTI regulation. This paper investigates blockchain adoption in maritime supply chains with a focus on ZKP integration for secure document verification, fraud mitigation, and regulatory compliance. It evaluates computational overhead, scalability, and adoption barriers, and proposes a framework supported by simulation-based validation using Ethereum and ZoKrates to assess feasibility and performance. By combining ZKPs with blockchain, this approach enhances a secure, transparent, and efficient trade ecosystem, optimising resources and reducing risks. Future research directions are outlined to advance sustainable maritime logistics.